Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
The Journal of Machine Learning Research
K-means clustering via principal component analysis
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Face Recognition Using Laplacianfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Eigenfunctions Links Spectral Embedding and Kernel PCA
Neural Computation
Dimensionality Reduction by Learning an Invariant Mapping
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Scalable Clustering Algorithms with Balancing Constraints
Data Mining and Knowledge Discovery
The Journal of Machine Learning Research
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
The Journal of Machine Learning Research
Maximum margin clustering made practical
Proceedings of the 24th international conference on Machine learning
Large scale manifold transduction
Proceedings of the 25th international conference on Machine learning
Nonlinear Dimensionality Reduction
Nonlinear Dimensionality Reduction
Handwritten Data Clustering Using Agents Competition in Networks
Journal of Mathematical Imaging and Vision
Uncovering overlapping cluster structures via stochastic competitive learning
Information Sciences: an International Journal
| Hi-index | 0.00 |
We present a new framework for large-scale data clustering. The main idea is to modify functional dimensionality reduction techniques to directly optimize over discrete labels using stochastic gradient descent. Compared to methods like spectral clustering our approach solves a single optimization problem, rather than an ad-hoc two-stage optimization approach, does not require a matrix inversion, can easily encode prior knowledge in the set of implementable functions, and does not have an "out-of-sample" problem. Experimental results on both artificial and real-world datasets show the usefulness of our approach.